Text Segmentation Using Exponential Models

TL;DR

This paper presents a novel statistical text segmentation method combining short- and long-range language models with lexical hints, evaluated using a new error metric across diverse news domains.

Contribution

It introduces a new probabilistic approach for text segmentation that integrates multiple language models and lexical cues, along with a novel evaluation metric.

Findings

Effective segmentation on news articles and transcripts

Outperforms traditional precision/recall metrics

Demonstrates robustness across different domains

Abstract

This paper introduces a new statistical approach to partitioning text automatically into coherent segments. Our approach enlists both short-range and long-range language models to help it sniff out likely sites of topic changes in text. To aid its search, the system consults a set of simple lexical hints it has learned to associate with the presence of boundaries through inspection of a large corpus of annotated data. We also propose a new probabilistically motivated error metric for use by the natural language processing and information retrieval communities, intended to supersede precision and recall for appraising segmentation algorithms. Qualitative assessment of our algorithm as well as evaluation using this new metric demonstrate the effectiveness of our approach in two very different domains, Wall Street Journal articles and the TDT Corpus, a collection of newswire articles and broadcast news transcripts.